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Methods for Mediation Analysis with Missing Data

Published online by Cambridge University Press:  01 January 2025

Zhiyong Zhang  and
Lijuan Wang
Zhiyong Zhang*
Affiliation:
University of Notre Dame
Lijuan Wang
Affiliation:
University of Notre Dame
*
Requests for reprints should be sent to Zhiyong Zhang, University of Notre Dame, Notre Dame, IN, USA. E-mail: [email protected]
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Abstract

Despite wide applications of both mediation models and missing data techniques, formal discussion of mediation analysis with missing data is still rare. We introduce and compare four approaches to dealing with missing data in mediation analysis including listwise deletion, pairwise deletion, multiple imputation (MI), and a two-stage maximum likelihood (TS-ML) method. An R package bmem is developed to implement the four methods for mediation analysis with missing data in the structural equation modeling framework, and two real examples are used to illustrate the application of the four methods. The four methods are evaluated and compared under MCAR, MAR, and MNAR missing data mechanisms through simulation studies. Both MI and TS-ML perform well for MCAR and MAR data regardless of the inclusion of auxiliary variables and for AV-MNAR data with auxiliary variables. Although listwise deletion and pairwise deletion have low power and large parameter estimation bias in many studied conditions, they may provide useful information for exploring missing mechanisms.

Keywords

mediation analysismissing dataMITS-MLbootstrapauxiliary variables
Type
Original Paper
Information
Psychometrika , Volume 78 , Issue 1 , January 2013 , pp. 154 - 184
Copyright
Copyright © The Psychometric Society

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